---
title: OpenAI
---

This will help you get started with OpenAIEmbeddings [embedding models](/oss/concepts/embedding_models) using LangChain. For detailed documentation on `OpenAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html).

## Overview

### Integration details

| Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/text_embedding/openai/) | Downloads | Version |
| :--- | :--- | :---: | :---: |  :---: | :---: |
| [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) | [@langchain/openai](https://api.js.langchain.com/modules/langchain_openai.html) | ❌ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/openai?style=flat-square&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/openai?style=flat-square&label=%20&) |

## Setup

To access OpenAIEmbeddings embedding models you'll need to create an OpenAI account, get an API key, and install the `@langchain/openai` integration package.

### Credentials

Head to [platform.openai.com](https://platform.openai.com) to sign up to OpenAI and generate an API key. Once you've done this set the `OPENAI_API_KEY` environment variable:

```bash
export OPENAI_API_KEY="your-api-key"
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```bash
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
```

### Installation

The LangChain OpenAIEmbeddings integration lives in the `@langchain/openai` package:

```{=mdx}
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/openai @langchain/core
</Npm2Yarn>
```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```typescript
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings = new OpenAIEmbeddings({
  apiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.OPENAI_API_KEY
  batchSize: 512, // Default value if omitted is 512. Max is 2048
  model: "text-embedding-3-large",
});
```

If you're part of an organization, you can set `process.env.OPENAI_ORGANIZATION` to your OpenAI organization id, or pass it in as `organization` when
initializing the model.

## Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [**Learn** tab](/oss/learn/).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document using the demo [`MemoryVectorStore`](/oss/integrations/vectorstores/memory).

```typescript
// Create a vector store with a sample text
import { MemoryVectorStore } from "langchain/vectorstores/memory";

const text = "LangChain is the framework for building context-aware reasoning applications";

const vectorstore = await MemoryVectorStore.fromDocuments(
  [{ pageContent: text, metadata: {} }],
  embeddings,
);

// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);

// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");

retrievedDocuments[0].pageContent;
```

```output
LangChain is the framework for building context-aware reasoning applications
```

## Direct Usage

Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

### Embed single texts

You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query:

```typescript
const singleVector = await embeddings.embedQuery(text);

console.log(singleVector.slice(0, 100));
```

```output
[
    -0.01927683,  0.0037708976,  -0.032942563,  0.0037671267,  0.008175306,
   -0.012511838,  -0.009713832,   0.021403614,  -0.015377721, 0.0018684798,
    0.020574018,   0.022399133,   -0.02322873,   -0.01524951,  -0.00504169,
   -0.007375876,   -0.03448109, 0.00015130726,   0.021388533, -0.012564631,
   -0.020031009,   0.027406884,  -0.039217334,    0.03036327,  0.030393435,
   -0.021750538,   0.032610722,  -0.021162277,  -0.025898525,  0.018869571,
    0.034179416,  -0.013371604,  0.0037652412,   -0.02146395, 0.0012641934,
   -0.055688616,    0.05104287,  0.0024982197,  -0.019095825, 0.0037369595,
  0.00088757504,   0.025189597,  -0.018779071,   0.024978427,  0.016833287,
  -0.0025868358,  -0.011727491, -0.0021154736,  -0.017738303, 0.0013839195,
  -0.0131151825,   -0.05405959,   0.029729757,  -0.003393808,  0.019774588,
    0.028885076,   0.004355387,   0.026094612,    0.06479911,  0.038040817,
    -0.03478276,  -0.012594799,  -0.024767255, -0.0031430433,  0.017874055,
   -0.015294761,   0.005709139,   0.025355516,   0.044798266,   0.02549127,
    -0.02524993, 0.00014553308,  -0.019427665,  -0.023545485,  0.008748483,
    0.019850006,  -0.028417485,  -0.001860938,   -0.02318348, -0.010799851,
     0.04793565, -0.0048983963,    0.02193154,  -0.026411368,  0.026426451,
   -0.012149832,   0.035355937,  -0.047814984,  -0.027165547, -0.008228099,
   -0.007737882,   0.023726488,  -0.046487626,  -0.007783133, -0.019638835,
     0.01793439,  -0.018024892,  0.0030336871,  -0.019578502, 0.0042837397
]
```

### Embed multiple texts

You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries:

```typescript
const text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs";

const vectors = await embeddings.embedDocuments([text, text2]);

console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));
```

```output
[
    -0.01927683,  0.0037708976,  -0.032942563,  0.0037671267,  0.008175306,
   -0.012511838,  -0.009713832,   0.021403614,  -0.015377721, 0.0018684798,
    0.020574018,   0.022399133,   -0.02322873,   -0.01524951,  -0.00504169,
   -0.007375876,   -0.03448109, 0.00015130726,   0.021388533, -0.012564631,
   -0.020031009,   0.027406884,  -0.039217334,    0.03036327,  0.030393435,
   -0.021750538,   0.032610722,  -0.021162277,  -0.025898525,  0.018869571,
    0.034179416,  -0.013371604,  0.0037652412,   -0.02146395, 0.0012641934,
   -0.055688616,    0.05104287,  0.0024982197,  -0.019095825, 0.0037369595,
  0.00088757504,   0.025189597,  -0.018779071,   0.024978427,  0.016833287,
  -0.0025868358,  -0.011727491, -0.0021154736,  -0.017738303, 0.0013839195,
  -0.0131151825,   -0.05405959,   0.029729757,  -0.003393808,  0.019774588,
    0.028885076,   0.004355387,   0.026094612,    0.06479911,  0.038040817,
    -0.03478276,  -0.012594799,  -0.024767255, -0.0031430433,  0.017874055,
   -0.015294761,   0.005709139,   0.025355516,   0.044798266,   0.02549127,
    -0.02524993, 0.00014553308,  -0.019427665,  -0.023545485,  0.008748483,
    0.019850006,  -0.028417485,  -0.001860938,   -0.02318348, -0.010799851,
     0.04793565, -0.0048983963,    0.02193154,  -0.026411368,  0.026426451,
   -0.012149832,   0.035355937,  -0.047814984,  -0.027165547, -0.008228099,
   -0.007737882,   0.023726488,  -0.046487626,  -0.007783133, -0.019638835,
     0.01793439,  -0.018024892,  0.0030336871,  -0.019578502, 0.0042837397
]
[
   -0.010181213,   0.023419594,   -0.04215527, -0.0015320902,  -0.023573855,
  -0.0091644935,  -0.014893179,   0.019016149,  -0.023475688,  0.0010219777,
    0.009255648,    0.03996757,   -0.04366983,   -0.01640774,  -0.020194141,
    0.019408813,  -0.027977299,  -0.022017224,   0.013539891,  -0.007769135,
    0.032647192,  -0.015089511,  -0.022900717,   0.023798235,   0.026084099,
   -0.024625633,   0.035003178,  -0.017978394,  -0.049615882,   0.013364594,
    0.031132633,   0.019142363,   0.023195215,  -0.038396914,   0.005584942,
   -0.031946007,   0.053682756, -0.0036356465,   0.011240003,  0.0056690844,
  -0.0062791156,   0.044146635,  -0.037387207,    0.01300699,   0.018946031,
   0.0050415234,   0.029618073,  -0.021750772,  -0.000649473, 0.00026951815,
   -0.014710871,  -0.029814405,    0.04204308,  -0.014710871,  0.0039616977,
   -0.021512369,   0.054608323,   0.021484323,    0.02790718,  -0.010573876,
   -0.023952495,  -0.035143413,  -0.048802506, -0.0075798146,   0.023279356,
   -0.022690361,  -0.016590048,  0.0060477243,   0.014100839,   0.005476258,
   -0.017221114, -0.0100059165,  -0.017922299,  -0.021989176,    0.01830094,
     0.05516927,   0.001033372,  0.0017310516,   -0.00960624,  -0.037864015,
    0.013063084,   0.006591143,  -0.010160177,  0.0011394264,    0.04953174,
    0.004806626,   0.029421741,  -0.037751824,   0.003618117,   0.007162609,
    0.027696826, -0.0021070621,  -0.024485396, -0.0042141243,   -0.02801937,
   -0.019605145,   0.016281527,  -0.035143413,    0.01640774,   0.042323552
]
```

## Specifying dimensions

With the `text-embedding-3` class of models, you can specify the size of the embeddings you want returned. For example by default `text-embedding-3-large` returns embeddings of dimension 3072:

```typescript
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddingsDefaultDimensions = new OpenAIEmbeddings({
  model: "text-embedding-3-large",
});

const vectorsDefaultDimensions = await embeddingsDefaultDimensions.embedDocuments(["some text"]);
console.log(vectorsDefaultDimensions[0].length);
```

```output
3072
```

But by passing in `dimensions: 1024` we can reduce the size of our embeddings to 1024:

```typescript
import { OpenAIEmbeddings } from "@langchain/openai";

const embeddings1024 = new OpenAIEmbeddings({
  model: "text-embedding-3-large",
  dimensions: 1024,
});

const vectors1024 = await embeddings1024.embedDocuments(["some text"]);
console.log(vectors1024[0].length);
```

```output
1024
```

## Custom URLs

You can customize the base URL the SDK sends requests to by passing a `configuration` parameter like this:

```typescript
import { OpenAIEmbeddings } from "@langchain/openai";

const model = new OpenAIEmbeddings({
  configuration: {
    baseURL: "https://your_custom_url.com",
  },
});
```

You can also pass other `ClientOptions` parameters accepted by the official SDK.

If you are hosting on Azure OpenAI, see the [dedicated page instead](/oss/integrations/text_embedding/azure_openai).

## API reference

For detailed documentation of all OpenAIEmbeddings features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html).
